bims-librar Biomed News
on Biomedical librarianship
Issue of 2024–12–15
eightteen papers selected by
Thomas Krichel, Open Library Society
  1. A web scraping app for smart literature search of the keywords.
  2. Utility of Chatbot Literature Search in Radiation Oncology.
  3. Evaluating the adoption of handsearching, citation chasing, and screening tools in education research: a survey study.
  4. Evaluation of RMES, an Automated Software Tool Utilizing AI, for Literature Screening with Reference to Published Systematic Reviews as Case-Studies: Development and Usability Study.
  5. Assessing AI Simplification of Medical Texts: Readability and Content Fidelity.
  6. Evaluating Artificial Intelligence (AI)-Generated Patient Education Guides on Epilepsy: A Cross-Sectional Study of ChatGPT and Google Gemini.
  7. Evaluating Quality and Readability of AI-generated Information on Living Kidney Donation.
  8. Impact of Demographic Modifiers on Readability of Myopia Education Materials Generated by Large Language Models.
  9. The impact of internet resources and artificial intelligence on information on myringotomy tubes.
  10. An Evaluation of the Readability, Quality, and Content of Online Disaster Preparedness Materials for the Society.
  11. Evaluating Readability and Reliability of Online Information Provided by Pancreas Transplant Centers in the United States.
  12. Identifying and Evaluating Online Kidney Stone Pain Resources: A Cross-Sectional Study.
  13. Global quality scores of Brazilian public health system-related YouTubeTM videos and their users' engagement.
  14. Assessment of Quality and Reliability of YouTube Videos for Student Learning on Class II Resin Composite Restorations.
  15. YouTube videos as an information source about exercises for temporomandibular disorders.
  16. Current status of short video as a source of information on lung cancer: a cross-sectional content analysis study.
  17. Quality of Pancreatic Neuroendocrine Tumor Videos Available on TikTok and Bilibili: Content Analysis.
  18. Exploring trust dynamics in health information systems: the impact of patients' health conditions on information source preferences.
  1. PeerJ Comput Sci. 2024 ;10 e2384
    A web scraping app for smart literature search of the keywords.
    Muhammed Ali Mutlu, Eyup Emre Ulku, Kazim Yildiz.
      Detailed literature search and writing is very important for the success of long research projects, publications and theses. Search engines provide significant convenience in research processes. However, conducting a comprehensive and systematic research on the web requires a long working process. In order to make literature searches effective, simple and comprehensive, various libraries and development tools have been created and made available. By using these development tools, research processes that may take days can be reduced to hours or even minutes. Literature review is not only necessary for academic studies, but it is a process that should be used and performed in every field where new approaches are adopted. Literature review is a process that gives us important ideas about whether similar studies have been conducted before, which methods have been used before and what has not been addressed in previous studies. It is also of great importance in terms of preventing possible copyright problems in future studies. The main purpose of this study is to propose an application that will facilitate, speed up and increase the efficiency of literature searches. In existing systems, literature searches are performed by browsing search sites or various article sites one by one and using the search tools provided by these sites. It is simple to use, allows the entire World Wide Web environment to be searched, and provides the user with the search findings. In this study, we have implemented an application that allows the crawling of the entire World Wide Web environment, is very simple to use, and quickly presents the crawl findings to the user.
    Keywords:  Information access; Knowledge discovery; Literature search framework; Scraping and crawling bots; Smart literature search; Web crawling; Web scraping
    DOI:  https://doi.org/10.7717/peerj-cs.2384
  2. J Cancer Educ. 2024 Dec 14.
    Utility of Chatbot Literature Search in Radiation Oncology.
    Justina Wong, Conley Kriegler, Ananya Shrivastava, Adele Duimering, Connie Le.
      Artificial intelligence and natural language processing tools have shown promise in oncology by assisting with medical literature retrieval and providing patient support. The potential for these technologies to generate inaccurate yet seemingly correct information poses significant challenges. This study evaluates the effectiveness, benefits, and limitations of ChatGPT for clinical use in conducting literature reviews of radiation oncology treatments. This cross-sectional study used ChatGPT version 3.5 to generate literature searches on radiotherapy options for seven tumor sites, with prompts issued five times per site to generate up to 50 publications per tumor type. The publications were verified using the Scopus database and categorized as correct, irrelevant, or non-existent. Statistical analysis with one-way ANOVA compared the impact factors and citation counts across different tumor sites. Among the 350 publications generated, there were 44 correct, 298 non-existent, and 8 irrelevant papers. The average publication year of all generated papers was 2011, compared to 2009 for the correct papers. The average impact factor of all generated papers was 38.8, compared to 113.8 for the correct papers. There were significant differences in the publication year, impact factor, and citation counts between tumor sites for both correct and non-existent papers. Our study highlights both the potential utility and significant limitations of using AI, specifically ChatGPT 3.5, in radiation oncology literature reviews. The findings emphasize the need for verification of AI outputs, development of standardized quality assurance protocols, and continued research into AI biases to ensure reliable integration into clinical practice.
    Keywords:  Artificial intelligence; Cancer; ChatGPT; Natural language processing; Radiation oncology
    DOI:  https://doi.org/10.1007/s13187-024-02547-1
  3. Front Res Metr Anal. 2024 ;9 1430355
    Evaluating the adoption of handsearching, citation chasing, and screening tools in education research: a survey study.
    Qiyang Zhang, Marta Pellegrini, Francesco Marsili, Amanda Neitzel.
       Introduction: The rapid development of software tools to assist systematic reviewers has led to varying degrees of adoption and selection among researchers. However, the actual usage patterns of these tools, their preferred features, and the criteria for selecting the most suitable tools remain unclear.
    Methods: To understand these aspects, we collected 175 responses from researchers across different continents.
    Results: In terms of handsearching, despite new tools developed, our findings reveal that manual handsearching remains prevalent among more than half of the participants. Databases are the most popular tools for citation searching, followed by citation management tools and spreadsheets. This reliance on citation management tools and spreadsheets is concerning as they are not specifically designed for systematic reviews. The primary factors influencing tool selection are the research environment and ease of use. Barriers stopping researchers from adopting alternative tools include limited awareness, challenges in learning new tools, and the financial costs associated with acquiring licenses. Moreover, researchers located in Europe show greater familiarity with a wider range of tools compared to their North American counterparts.
    Discussion: This preregistered study contributes valuable insights into the tool usage patterns of education researchers, emphasizing the importance of promoting awareness and facilitating the broader adoption of existing tools.
    Keywords:  citation searching tools; education; handsearching tools; meta-analysis; screening tools; survey; systematic reviews
    DOI:  https://doi.org/10.3389/frma.2024.1430355
  4. JMIR Form Res. 2024 Dec 09. 8 e55827
    Evaluation of RMES, an Automated Software Tool Utilizing AI, for Literature Screening with Reference to Published Systematic Reviews as Case-Studies: Development and Usability Study.
    Ayaka Sugiura, Satoshi Saegusa, Yingzi Jin, Riki Yoshimoto, Nicholas D Smith, Koji Dohi, Tadashi Higuchi, Tomotake Kozu.
       BACKGROUND: Systematic reviews and meta-analyses are important to evidence-based medicine, but the information retrieval and literature screening procedures are burdensome tasks. Rapid Medical Evidence Synthesis (RMES; Deloitte Tohmatsu Risk Advisory LLC) is a software designed to support information retrieval, literature screening, and data extraction for evidence-based medicine.
    OBJECTIVE: This study aimed to evaluate the accuracy of RMES for literature screening with reference to published systematic reviews.
    METHODS: We used RMES to automatically screen the titles and abstracts of PubMed-indexed articles included in 12 systematic reviews across 6 medical fields, by applying 4 filters: (1) study type; (2) study type + disease; (3) study type + intervention; and (4) study type + disease + intervention. We determined the numbers of articles correctly included by each filter relative to those included by the authors of each systematic review. Only PubMed-indexed articles were assessed.
    RESULTS: Across the 12 reviews, the number of articles analyzed by RMES ranged from 46 to 5612. The number of PubMed-cited articles included in the reviews ranged from 4 to 47. The median (range) percentage of articles correctly labeled by RMES using filters 1-4 were: 80.9% (57.1%-100%), 65.2% (34.1%-81.8%), 70.5% (0%-100%), and 58.6% (0%-81.8%), respectively.
    CONCLUSIONS: This study demonstrated good performance and accuracy of RMES for the initial screening of the titles and abstracts of articles for use in systematic reviews. RMES has the potential to reduce the workload involved in the initial screening of published studies.
    Keywords:  RMES; Rapid Medical Evidence Synthesis; artificial intelligence; automated literature screening; natural language processing; randomized controlled trials; systematic reviews; text mining
    DOI:  https://doi.org/10.2196/55827
  5. Int J Med Inform. 2024 Dec 01. pii: S1386-5056(24)00406-4. [Epub ahead of print]195 105743
    Assessing AI Simplification of Medical Texts: Readability and Content Fidelity.
    Bryce Picton, Saman Andalib, Aidin Spina, Brandon Camp, Sean S Solomon, Jason Liang, Patrick M Chen, Jefferson W Chen, Frank P Hsu, Michael Y Oh.
       INTRODUCTION: The escalating complexity of medical literature necessitates tools to enhance readability for patients. This study aimed to evaluate the efficacy of ChatGPT-4 in simplifying neurology and neurosurgical abstracts and patient education materials (PEMs) while assessing content preservation using Latent Semantic Analysis (LSA).
    METHODS: A total of 100 abstracts (25 each from Neurosurgery, Journal of Neurosurgery, Lancet Neurology, and JAMA Neurology) and 340 PEMs (66 from the American Association of Neurological Surgeons, 274 from the American Academy of Neurology) were transformed by a GPT-4.0 prompt requesting a 5th grade reading level. Flesch-Kincaid Grade Level (FKGL) and Flesch Reading Ease (FKRE) scores were used before/after transformation. Content fidelity was validated via LSA (ranging 0-1, 1 meaning identical topics) and by expert assessment (0-1) for a subset (n = 40). Pearson correlation coefficient compared assessments.
    RESULTS: FKGL decreased from 12th to 5th grade for abstracts and 13th to 5th for PEMs (p < 0.001). FKRE scores showed similar improvement (p < 0.001). LSA confirmed high content similarity for abstracts (mean cosine similarity 0.746) and PEMs (mean 0.953). Expert assessment indicated a mean topic similarity of 0.775 for abstracts and 0.715 for PEMs. The Pearson coefficient between LSA and expert assessment of textual similarity was 0.598 for abstracts and -0.167 for PEMs. Segmented analysis of similarity correlations revealed a correlation of 0.48 (p = 0.02) below 450 words and a -0.20 (p = 0.43) correlation above 450 words.
    CONCLUSION: GPT-4.0 markedly improved the readability of medical texts, predominantly maintaining content integrity as substantiated by LSA and expert evaluations. LSA emerged as a reliable tool for assessing content fidelity within moderate-length texts, but its utility diminished for longer documents, overestimating similarity. These findings support the potential of AI in combating low health literacy, however, the similarity scores indicate expert validation is crucial. Future research must strive to improve transformation precision and develop validation methodologies.
    Keywords:  ChatGPT; Health literacy; Latent semantic analysis; Readability
    DOI:  https://doi.org/10.1016/j.ijmedinf.2024.105743
  6. Cureus. 2024 Nov;16(11): e73212
    Evaluating Artificial Intelligence (AI)-Generated Patient Education Guides on Epilepsy: A Cross-Sectional Study of ChatGPT and Google Gemini.
    Shiv Arora, Meghna Ramesh, Aye Thandar Moe, Tapan Giri, Kaksha Parrikh, Hima Varsha Challa.
      Introduction Epilepsy is a chronic disorder that requires patient education for management and to avoid triggers and complications. This study aims to evaluate and compare the effectiveness of two artificial intelligence (AI) tools, ChatGPT (version 3.5, OpenAI, Inc., San Francisco, United States) and Google Gemini (version 1.5, Google LLC, Mountain View, California, United States), in generating patient education guides for epilepsy disorders. Methodology A patient education guide was generated on ChatGPT and Google Gemini. The study analyzed the sentence count, readability, and ease of understanding using the Flesch-Kincaid calculator, examined similarity using the QuillBot plagiarism tool, and assessed reliability using a modified DISCERN score. Statistical analysis included an unpaired T-test where a P-value <0.05 is considered significant. Results There was no statistically significant difference between ChatGPT and Google Gemini in terms of word count (p=0.75), sentence count (p=0.96), average words per sentence (p=0.66), grade level (p=0.67), similarity% (p=0.57), and reliability scores (p=0.42). Ease scores generated by ChatGPT and Google Gemini were 38.6 and 43.6 for generalized tonic-clonic seizures (GTCS), 18.7 and 45.5 for myoclonic seizures, and 22.4 and 55.8 for status epilepticus, respectively, showing Google Gemini generated responses notably better (p=0.0493). The average syllables per word (p=0.035) were appreciably lower for Google Gemini-generated responses, with 1.8 for GTCS and myoclonic, 1.7 for status epilepticus against 1.9 for GTCS, 2 for myoclonic, and 2.1 for status epilepticus for ChatGPT responses. Conclusions A significant difference was seen in only two parameters. Further improvement in AI tools is necessary to provide effective guides.
    Keywords:  artificial intelligence; chatgpt; education guide; epilepsy; generalized tonic-clonic seizures (gtcs); google gemini; myoclonic seizures; seizures; status epilepticus
    DOI:  https://doi.org/10.7759/cureus.73212
  7. Transplant Direct. 2025 Jan;11(1): e1740
    Evaluating Quality and Readability of AI-generated Information on Living Kidney Donation.
    Vincenzo Villani, Hong-Hanh T Nguyen, Kumaran Shanmugarajah.
       Background: The availability of high-quality and easy-to-read informative material is crucial to providing accurate information to prospective kidney donors. The quality of this information has been associated with the likelihood of proceeding with a living donation. Artificial intelligence-based large language models (LLMs) have recently become common instruments for acquiring information online, including medical information. The aim of this study was to assess the quality and readability of artificial intelligence-generated information on kidney donation.
    Methods: A set of 35 common donor questions was developed by the authors and used to interrogate 3 LLMs (ChatGPT, Google Gemini, and MedGPT). Answers were collected and independently evaluated using the CLEAR tool for (1) completeness, (2) lack of false information, (3) evidence-based information, (4) appropriateness, and (5) relevance. Readability was evaluated using the Flesch-Kincaid Reading Ease Score and the Flesch-Kincaid Grade Level.
    Results: The interrater intraclass correlation was 0.784 (95% confidence interval, 0.716-0.814). Median CLEAR scores were ChatGPT 22 (interquartile range [IQR], 3.67), Google Gemini 24.33 (IQR, 2.33), and MedGPT 23.33 (IQR, 2.00). ChatGPT, Gemini, and MedGPT had mean Flesch-Kincaid Reading Ease Scores of 37.32 (SD = 10.00), 39.42 (SD = 13.49), and 29.66 (SD = 7.94), respectively. Using the Flesch-Kincaid Grade Level assessment, ChatGPT had an average score of 12.29, Gemini had 10.63, and MedGPT had 13.21 (P < 0.001), indicating that all LLMs had a readability at the college-level education.
    Conclusions: Current LLM provides fairly accurate responses to common prospective living kidney donor questions; however, the generated information is complex and requires an advanced level of education. As LLMs become more relevant in the field of medical information, transplant providers should familiarize themselves with the shortcomings of these technologies.
    DOI:  https://doi.org/10.1097/TXD.0000000000001740
  8. Clin Ophthalmol. 2024 ;18 3591-3604
    Impact of Demographic Modifiers on Readability of Myopia Education Materials Generated by Large Language Models.
    Gabriela G Lee, Deniz Goodman, Ta Chen Peter Chang.
       Background: The rise of large language models (LLM) promises to widely impact healthcare providers and patients alike. As these tools reflect the biases of currently available data on the internet, there is a risk that increasing LLM use will proliferate these biases and affect information quality. This study aims to characterize the effects of different race, ethnicity, and gender modifiers in question prompts presented to three large language models (LLM) on the length and readability of patient education materials about myopia.
    Methods: ChatGPT, Gemini, and Copilot were provided a standardized prompt incorporating demographic modifiers to inquire about myopia. The races and ethnicities evaluated were Asian, Black, Hispanic, Native American, and White. Gender was limited to male or female. The prompt was inserted five times into new chat windows. Responses were analyzed for readability by word count, Simple Measure of Gobbledygook (SMOG) index, Flesch-Kincaid Grade Level, and Flesch Reading Ease score. Significant differences were analyzed using two-way ANOVA on SPSS.
    Results: A total of 150 responses were analyzed. There were no differences in SMOG index, Flesch-Kincaid Grade Level, or Flesch Reading Ease scores between responses generated with prompts containing different gender, race, or ethnicity modifiers using ChatGPT or Copilot. Gemini-generated responses differed significantly in their SMOG Index, Flesch-Kincaid Grade Level, and Flesch Reading Ease based on the race mentioned in the prompt (p<0.05).
    Conclusion: Patient demographic information impacts the reading level of educational material generated by Gemini but not by ChatGPT or Copilot. As patients use LLMs to understand ophthalmologic diagnoses like myopia, clinicians and users should be aware of demographic influences on readability. Patient gender, race, and ethnicity may be overlooked variables affecting the readability of LLM-generated education materials, which can impact patient care. Future research could focus on the accuracy of generated information to identify potential risks of misinformation.
    Keywords:  health literacy; large language models; readability
    DOI:  https://doi.org/10.2147/OPTH.S483024
  9. Eur Arch Otorhinolaryngol. 2024 Dec 12.
    The impact of internet resources and artificial intelligence on information on myringotomy tubes.
    Melissa Papuc, Patrick Scheffler.
       PURPOSE: To compare the quality and readability of patient education materials on myringotomy tubes from artificial intelligence and Google search.
    METHODS: Three questions were posed to ChatGPT and Google Gemini addressing "Condition," "Investigation," and "Treatment" domains. Google was queried for "Ear tubes," "Myringotomy and tubes," and "Tympanostomy tubes." Text quality was assessed using the DISCERN instrument. Readability was assessed using the Flesch-Kincaid Grade Level, Flesch-Kincaid Reading Ease scores, and the Fry Readability Graph.
    RESULTS: The average DISCERN score for websites was 52 (SD = 13.1, Median = 55.5), out of 80. The mean Flesch-Kincaid Reading Grade Level was 8 (SD = 3, Median = 7.1), and the mean Flesch-Kincaid Reading Ease score was 55 (SD = 12.3, Median = 57.7). ChatGPT and Google Gemini's "Condition" responses each had DISCERN scores of 46, Flesch-Kincaid Grade Levels of 13.1 and 9.5, and Reading Ease scores of 41 and 61. For "Investigation," DISCERN scores were 46 (ChatGPT) and 66 (Google Gemini), Grade Levels were 13.9 and 12.4, and Reading Ease scores were 38.9 and 34.9. For "Treatment," ChatGPT and Google Gemini had DISCERN scores of 45 and 34, Grade Levels of 15.7 and 9.8, and Reading Ease scores of 36.2 and 53.9.
    CONCLUSION: Sites and artificial intelligence providing patient education material regarding myringotomy tubes are of "fair" quality but have readability levels above the recommended 6th grade level. Google search results were superior to artificial intelligence in readability.
    Keywords:  Artificial intelligence; ChatGPT; Google Gemini; Google search; Myringotomy tubes; Patient education
    DOI:  https://doi.org/10.1007/s00405-024-09148-0
  10. Disaster Med Public Health Prep. 2024 Dec 10. 18 e304
    An Evaluation of the Readability, Quality, and Content of Online Disaster Preparedness Materials for the Society.
    Bengisu Karagöz, F Nehir Öznur Muz, Sibel Sert, Oğuz Han Aydilek, M Amine Altındag, Selma Metintaş, M Fatih Önsüz.
       OBJECTIVES: A useful way to prepare the public for disasters is to teach them where to get information. The purpose of this study is to evaluate the readability and appropriateness of the content of websites prepared for the public on disaster preparedness.
    METHODS: In September-October 2022, we evaluated 95 disaster preparedness websites (intended for the public) using the Ateşman Readability Index, JAMA criteria, DISCERN, and a new researcher-created content comparison form. Evaluation scores were compared according to information sources.
    RESULTS: Of the websites included in the research, 45.2% represented government institutions (GIG), 38.0% non-profit organizations (NPOG), 8.4% municipal organizations (MOG), and 8.4% other organizations (OG). Those which scored above average on the websites were 36.8% on the content evaluation, 51.6% on the DISCERN scale, 53.7% on the Ateşman Readability Index, and 55.8% on the JAMA criteria. The content evaluation form showed that the scores of the websites belonging to the MOG were higher than the scores of the other websites. Others group websites also scored higher than altered websites on the JAMA criteria.
    CONCLUSIONS: The study revealed that websites created to increase public knowledge on disaster preparedness are not good enough in terms of readability, quality, and content.
    Keywords:  Internet; disaster management; natural disasters; readability
    DOI:  https://doi.org/10.1017/dmp.2024.310
  11. Clin Transplant. 2024 Dec;38(12): e70055
    Evaluating Readability and Reliability of Online Information Provided by Pancreas Transplant Centers in the United States.
    Vincenzo Villani, Kumaran Shanmugarajah.
       BACKGROUND: The decision of proceeding with a pancreas transplant (PTx) is a complex one, and patient education is important to allow transplant candidates to make an informed and autonomous decision. In this study, we assessed the readability and reliability of online information provided by PTx centers in the United States.
    METHODS: Websites of PTx centers active between 2022 and 2023 were searched for patient information on pancreas transplantation. Readability was assessed using eight validated formulas. Reliability was assessed using the Journal of the American Medical Association criteria.
    RESULTS: Of 117 PTx centers, 57 provided online information material. High-volume centers were more likely to provide patient information compared to medium- and low-volume centers (76.6%, 45.6%, 34.6%, respectively). Average readability was at the 11th grade and beyond, with no difference among the three groups (11.9, 11.4, 11.4). Reliability was low, with two centers providing information on the material sources. Only one center provided information in a language other than English.
    CONCLUSIONS: Readability of online material on PTx provided by US transplant centers is low, well above the recommended 6th grade-level. Transplant centers, national societies and patient advocacy groups should collaborate in developing information material that is evidence-based, easy to read, and available in multiple languages.
    DOI:  https://doi.org/10.1111/ctr.70055
  12. Cureus. 2024 Nov;16(11): e73140
    Identifying and Evaluating Online Kidney Stone Pain Resources: A Cross-Sectional Study.
    Christopher J Hernandez, Jonathan Lim, Rebecca Takele, Frankie Escobedo, Georgina Dominique, Leslie Ojeaburu, Kymora Scotland.
      Introduction and objectives With the continuous growth of social media platforms, an increasing number of individuals are turning to them as their main source of medical information. This article aims to pinpoint the most widely accessed online resources on kidney stone pain among those who suffer from kidney stones and to assess the reliability, understandability, quality, and actionability of their content. Materials and methods The social media analysis platform BuzzSumo was employed to identify pertinent articles and assess their engagement levels. The DISCERN instrument was utilized to evaluate the quality of the top 10 most popular articles, while online software for determining reading grade levels was used to assess article readability. Additionally, the PEMAT (Patient Education Material Assessment Tool) was employed to gauge the actionability and understandability of these articles. Results The BuzzSumo results were obtained through four search categories: "passing stones," "home remedies," "stent pain," and "back pain." DISCERN exhibited a mean article score of 2.67/5, indicating low quality. The average reading grade level for articles was 10.4, with a median of 10. PEMAT results indicated an average understandability score of 60%, signifying that most articles were not easily understandable, and an average actionability score of 31%, indicating a lack of actionable steps in the majority of articles to improve health outcomes. Conclusions Online resources about kidney stone pain were found to have shortcomings, including content with a reading level higher than average, and a lack of actionable solutions for improving overall health.
    Keywords:  kidney stone; kidney stone pain; kidney stone resources; nephrolithiasis; stent pain; stent-related-pain
    DOI:  https://doi.org/10.7759/cureus.73140
  13. Braz Oral Res. 2024 ;pii: S1806-83242024000103012. [Epub ahead of print]38 e099
    Global quality scores of Brazilian public health system-related YouTubeTM videos and their users' engagement.
    Eliane Maria Mascarenhas Silva, Caroline Rabelo Camargos, Isabela Almeida Pordeus, Mauro Henrique Nogueira Guimarães de Abreu, Fabiana Vargas-Ferreira, Flávio Freitas Mattos.
      The study assessed the Global Quality Score (GQS) and informational engagement of users with YouTube videos on the Brazilian public health system (SUS). The YouTube video search tool was used with the Portuguese keywords 'unified health system' and 'SUS'. The first 100 videos returned in the search were studied, using the GQS to measure their educational value, usefulness, and information quality. Users' engagement with the videos was calculated based on their number of likes/reactions and comments. Other data collected were authorship, year of publication, topic approached, target audience, video length, and use of references. Two trained and calibrated researchers collected the data. Multiple analysis was performed with Logistic Regression, using a 95% confidence interval and significance of p<0.05. There were no poor or generally poor GQS scores (scores 1 and 2) and most videos (58%) achieved moderate or good scores (scores 3 and 4). Videos published after the onset of COVID-19 had a 70% lower chance of engagement than those published in pre-pandemic years (OR: 0.30; 95%CI: 0.12-0.74). Videos that targeted healthcare professionals were 72% less likely to achieve higher GQS scores, than those with an unidentified target audience (OR: 0.28; 95%CI: 0.10-0.75). The informational engagement of the videos showed fewer comments than likes/reactions. Most YouTube videos about the SUS had moderate or good global quality, which was associated with their period of publication and choice of target audience.
    DOI:  https://doi.org/10.1590/1807-3107bor-2024.vol38.0099
  14. Oper Dent. 2024 Dec 10.
    Assessment of Quality and Reliability of YouTube Videos for Student Learning on Class II Resin Composite Restorations.
    A A Balhaddad, I Alkhalifah, D Albuhmdouh, R AlSheikh, L Al Dehailan, H AlQuorain, A A Alsulaiman.
      YouTube has emerged as a popular platform for accessing educational content. However, its effectiveness has been a topic of debate in dental education. This study aimed to analyze the content and quality of YouTube videos focusing on Class II resin composite restorations. The first 100 videos of Class II resin composite information on YouTube were evaluated. The overall quality of the videos was assessed using the video information and quality index (VIQI) and Content Score based on Class II resin composite criteria. Videos with a score of less than the mean were recognized as low-content. No significant differences were observed between high- and low-content videos when the number of views, likes, duration, days since upload, viewing rate, and interaction index were investigated (p>0.05). The number of subscribers revealed a marginally significant difference (p=0.053). The high-content videos demonstrated higher mean values compared to the low-content videos in flow (4.6 vs. 3.8; p=0.0004), accuracy (4.3 vs. 3.3; p<0.0001), value (3.7 vs. 2.9; p=0.002), and precision (4.8 vs. 4.2; p=0.0002). The overall VIQI score was significantly higher (p<0.0001) in high-content videos (Mean 17.4; SD 1.5) compared to the low-content videos (Mean 14.2; SD 2.2). When the Content Score was assessed, high-content videos (Mean 9.9; SD 1.3) revealed a higher score (p<0.0001) than low-content-videos (Mean 4.2; SD 2.3). Most YouTube videos showcasing Class II resin composites serve as effective teaching tools. However, a significant number of videos with low content exist. Therefore, dental students should exercise caution when utilizing YouTube videos for learning purposes.
    DOI:  https://doi.org/10.2341/24-118-L
  15. Dent Med Probl. 2024 Nov-Dec;61(6):61(6): 855-863
    YouTube videos as an information source about exercises for temporomandibular disorders.
    Özlem Nur Tok, Ayşe Nihal Yurttaş, Serkan Taş.
       BACKGROUND: Temporomandibular disorders (TMD) are musculoskeletal and/or neuromuscular conditions that affect the muscles, joints and associated structures of the stomatognathic system.
    OBJECTIVES: This study aimed to evaluate the quality and reliability of publicly available English-language videos on YouTube about TMD exercises, and to examine the video sources and professional groups responsible for the creation of the videos.
    MATERIAL AND METHODS: The quality and reliability of the YouTube videos related to TMD exercises were evaluated using the DISCERN score, the global quality scale (GQS) and the JAMA (Journal of the American Medical Association) score.
    RESULTS: Of the 121 videos evaluated, 30 (24.8%) were uploaded by professional organizations, 49 (40.5%) by health information websites, and 42 (34.7%) were uploaded by independent users. Professional organizations had a significantly higher number of subscribers, likes, comments, and views than healthcare webpages and independent users (p < 0.001). The duration of videos uploaded by independent users was significantly longer than that of videos uploaded by healthcare webpages (p = 0.018). With regard to the profession of the video narrators, the unspecified group exhibited significantly lower JAMA (p < 0.001), GQS (p = 0.011) and DISCERN scores (p = 0.002) compared to chiropractors, physiotherapists, physicians, and other healthcare professionals. The JAMA scores for physicians, personal trainers and chiropractors were significantly lower than those for other healthcare professionals (p < 001). The JAMA score was positively correlated with the GQS (r = 0.469, p < 0.001) and DISCERN (r = 0.505, p < 0.001) scores. Similarly, the DISCERN score was positively correlated with the GQS score (r = 0.924, p < 0.001).
    CONCLUSIONS: Despite the abundance of video content on YouTube about TMD exercises, the quality of these videos is low, and their reliability is questionable.
    Keywords:  Internet; YouTube; exercises; health education; temporomandibular disorders
    DOI:  https://doi.org/10.17219/dmp/170922
  16. Front Oncol. 2024 ;14 1420976
    Current status of short video as a source of information on lung cancer: a cross-sectional content analysis study.
    Xinyu Zhao, Xinyi Yao, Binbin Sui, Yutao Zhou.
       Background: The morbidity and mortality rates of lung cancer continue to rise, leading to a significant disease burden. Health education on lung cancer serves as an effective approach for prevention and treatment. With the increasing popularity of the Internet, an escalating number of patients are turning to video platforms for health information. Short videos facilitate better absorption and retention of information, thus becoming the primary channel for health education communication. However, the quality of information provided in videos on these platforms remains uncertain. Therefore, this study aims to assess the information quality pertaining to lung cancer in short videos available on a Chinese video platform.
    Methods: Lung cancer-related videos on two short video platforms (TikTok and Kwai) were screened, and only Chinese (Mandarin) videos were included. The Global Quality Score (GQS) and modified DISCERN (mDISCERN) tools were then used to evaluate the quality and reliability of the information. A comparative analysis was conducted on videos from various sources. Additionally, correlation analysis was employed to investigate the factors influencing video quality.
    Results: After screening, a total of 186 videos were included. The median GQS score and mDISCERN score were 3 (IQR: 3-4) and 2 (IQR: 2-4), respectively. A total of 44.1% of the lung cancer videos provided a comprehensive explanation of the symptoms, while only 3.2% fully explanation the complications associated with lung cancer. Health professionals, particularly specialists, demonstrated higher quality video information compared to individual users (P<0.001). The correlation coefficient between GQS score and mDISCERN score was 0.340, showing a significant positive correlation (P<0.001). In addition, GQS score was positively correlated with video duration (r=0.177, P=0.015).
    Conclusion: The information quality of the 186 videos screened by the two platforms in this study was generally unsatisfactory. However, videos provided by experts were deemed relatively reliable, with video duration being closely associated with information quality. Therefore, it is crucial to meticulously screen high-quality and dependable videos on the platform in order to effectively guide lung cancer prevention and treatment.
    Keywords:  health education; health information; lung cancer; medical knowledge; short video
    DOI:  https://doi.org/10.3389/fonc.2024.1420976
  17. JMIR Form Res. 2024 Dec 11. 8 e60033
    Quality of Pancreatic Neuroendocrine Tumor Videos Available on TikTok and Bilibili: Content Analysis.
    Zheyu Niu, Yijie Hao, Faji Yang, Qirong Jiang, Yupeng Jiang, Shizhe Zhang, Xie Song, Hong Chang, Xu Zhou, Huaqiang Zhu, Hengjun Gao, Jun Lu.
       Background: Disseminating disease knowledge through concise videos on various platforms is an innovative and efficient approach. However, it remains uncertain whether pancreatic neuroendocrine tumor (pNET)-related videos available on current short video platforms can effectively convey accurate and impactful information to the general public.
    Objective: Our study aims to extensively analyze the quality of pNET-related videos on TikTok and Bilibili, intending to enhance the development of pNET-related social media content to provide the general public with more comprehensive and suitable avenues for accessing pNET-related information.
    Methods: A total of 168 qualifying videos pertaining to pNETs were evaluated from the video-sharing platforms Bilibili and TikTok. Initially, the fundamental information conveyed in the videos was documented. Subsequently, we discerned the source and content type of each video. Following that, the Global Quality Scale (GQS) and modified DISCERN (mDISCERN) scale were employed to appraise the educational value and quality of each video. A comparative evaluation was conducted on the videos obtained from these two platforms.
    Results: The number of pNET-related videos saw a significant increase since 2020, with 9 videos in 2020, 19 videos in 2021, 29 videos in 2022, and 106 videos in 2023. There were no significant improvements in the mean GQS or mDISCERN scores from 2020 to 2023, which were 3.22 and 3.00 in 2020, 3.33 and 2.94 in 2021, 2.83 and 2.79 in 2022, and 2.78 and 2.94 in 2023, respectively. The average quality scores of the videos on Bilibili and Tiktok were comparable, with GQS and mDISCERN scores of 2.98 on Bilibili versus 2.77 on TikTok and 2.82 on Bilibili versus 3.05 on TikTok, respectively. The source and format of the videos remained independent factors affecting the two quality scores. Videos that were uploaded by professionals (hazard ratio=7.02, P=.002) and recorded in specialized popular science formats (hazard ratio=12.45, P<.001) tended to exhibit superior quality.
    Conclusions: This study demonstrates that the number of short videos on pNETs has increased in recent years, but video quality has not improved significantly. This comprehensive analysis shows that the source and format of videos are independent factors affecting video quality, which provides potential measures for improving the quality of short videos.
    Keywords:  Bilibili; TikTok; pancreatic neuroendocrine tumors; quality analysis; short videos; social media
    DOI:  https://doi.org/10.2196/60033
  18. Front Public Health. 2024 ;12 1478502
    Exploring trust dynamics in health information systems: the impact of patients' health conditions on information source preferences.
    Mingming Song, Joel Elson, Tin Nguyen, Sharon Obasi, John Pintar, Dhundy Bastola.
       Introduction: Health information systems (HISs) should provide accessible and high-quality information to patients. However, the challenge lies in understanding patients' trust preferences for health information. This study explores how different information sources (e.g., online platforms, interpersonal sources) are trusted under varying health conditions, focusing on symptom intensity and disease type.
    Methods: Using a 2 × 2 × 4 between-subject design, 243 participants from a US college were presented with vignettes of acute or chronic diseases with varying symptom intensities and information sources. Participants rated their trust levels, including both cognitive and behavioral trust, in the health information and recommendations provided by one of the information sources, which was randomly assigned. Logistic regression and ANOVA were employed for the statistical analysis.
    Results: The analysis results revealed that trust is generally higher for interpersonal sources like doctors and family/friends compared to online sources like WebMD and Wikipedia when patients are making health decisions. Doctors are the most trusted source during health-related decision making. However, there are no significant differences in cognitive trust among interpersonal sources or among online sources. Furthermore, symptom intensity and disease type did not significantly alter trust levels across various information sources. These findings suggest that people prefer professional medical advice regardless of their health conditions.
    Discussion: The study highlights the need for HIS to incorporate features that provide "doctor-verified" information and promote interactive engagement to enhance patients' trust in information source. Additionally, it distinguishes between cognitive and behavioral trust, revealing distinct trust patterns that can inform the strategic development of HIS for varied health conditions. Understanding these trust dynamics can inform the design of effective, patient-centered HIS that better support health education, information seeking, and decision-making.
    Keywords:  behavioral trust; cognitive trust; disease type; health information system; information sources; symptom intensity; trust preferences
    DOI:  https://doi.org/10.3389/fpubh.2024.1478502
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